Manmade-Target Three-Dimensional Reconstruction Using Multi-View Radar Images

Manmade-target three-dimensional (3D) reconstruction is an attractive topic and also a challenge in radar imaging field. The factorization based 3D reconstruction algorithm using multi-view radar images provides a way without extra configuration requirements. The precondition of it is that the reconstructed points' positions are retrievable in all images, but it is hard to be satisfied in practice due to the points loss problem caused by occlusion and scattering variation during view change. The points loss problem reduces the reconstructed points and weaken the details. We propose a modi-fied manmade-target 3D reconstruction algorithm. Firstly, we calculate homography matrix after feature matching of each adjacent image pair and improve its accuracy through non-linear optimization. Then, we put forward a track generation algorithm under the guidance of the homography matrix to estimate strong points' positions in each image and a track filter to refine the estimation. Finally, we achieve the 3D coordinates through the orthographic factorization method. The measured data processing results demonstrate the validity and the reconstruction quality improvement of the proposed method.